\chapter{Results}
Investigating the potential of the proposed concept demands on fundamental testing sessions. This chapter shows first of all an accuracy test comparing centroid and CoG. These two figures are crucial in the position determination process of the overall FDP concept. After that it shows a \textit{path loss} investigation in a GSM and a UMTS environment, and reveals some details why the signal strength cannot be utilised in connection with the position determination process, proposed in this work.

\section{Accuracy test Centroid and CoG}
Introducing into the test setup, this section then presents accuracy results of two independent testing sessions in conjunction with the centroid and CoG. One session was conducted by car in a (sub)urban area, whereas the second session was executed without any means of transportation by walking only in an urban area. Finally it discusses the results of both testing sessions, and compares the results with similar LDTs.

\subsection{Test Setup}
This test was conducted in a \textbf{UMTS} environment. The used PLMN belongs to \textit{Hutchison 3G Austria} (Drei). Cell information of the area of Dornbirn (Austria) have been recorded. The area covers about 20 square kilometers and is defined by the 'geodecimal' coordinates listed in table \ref{definitionAreaOfInterest}.

\begin{table}[h]
\caption{Definition of covered area.}
\begin{tabular}{ll}
%
\\
\textbf{Longitude}&
\textbf{Latitude}& \hline
%
9.7668 &
47.3744 &
%
9.6854 &
47.3897 &
%
9.6608 &
47.4202 & 
%
9.7082 &
47.4137 & 
%
9.7082 &
47.4137 & 
%
9.7341 &
47.4430 & 
%
9.7614 &
47.4421 & 
%
9.7594 &
47.3982 & 
%
9.7085 &
47.3829 & 
%
9.6878 &
47.3672 & \hline
%
\end{tabular}
\label{definitionAreaOfInterest}
\end{table}

Most of the handover data have been fetched by cruising around by car in the area of interest. In suburban areas cell information along main roads have been considered whereas in urban area side roads besides main roads have been selected as well. Furthermore parts of pedestrian areas have been passed in order to improve the data consistency particularly in metropolitan areas. Drei provides a UMTS service in the whole area of interest, and 62 cells in this region have been detected. 261 handovers provide the basis in order to approximate the position of mobile users in this test. 

In order to verify the recorded samples the GPS position was continually recorded every 100 meters. This action permits the tracking of the used route, and subsequently the control of an approximate correlation between a sample's GPS position with one of the route. Hence if the sample's GPS position corresponds with an approximate GPS position of the route, the test figures will be included in ongoing test calculations.

\begin{lstlisting}[label=sampleRecord, caption={Overview Sample record.}]
<sample type="static" date="2006-08-02T20:18:01.56">
 <cell id="1532318" areacode="9000" mcc="232" mnc="10" status="2"/>
 <signalstrength>83</signalstrength>
 <gps>
  <longitude>9.74500500</longitude>
  <latitude>47.41636167</latitude>
  <status>1</status>
  <utctime>18:18:36.937</utctime>
  <speedInCmH>0.000</speedInCmH>
  <altitude>437.7</altitude>
  <altitudeunits>m</altitudeunits>
  <geoidseparation>48.1</geoidseparation>
  <geoidseparationunits>m</geoidseparationunits>
 </gps>
</sample>
\end{lstlisting}

\noindent
Samples have been recorded, and stored in a file on the MT like handovers. One sample record, shown in listing \ref{sampleRecord}, consists of the current cell information, the signal (field) strength and the appropriate GPS position, defined in the Samples XML-Schema. Further information about this XML-Schema can be looked up in appendix A.

For the calculation of the results the system queries the centroid or CoG of the cell information, stored in the sample record, and combines both figures with the GPS position, saved in the sample, to determine the inaccuracy.

The distance between the \textit{estimated} position, CoG or centroid stored in the database, and the \textit{real} position, position of the sample, represents the inaccuracy of this LDT. The distance is calculated based on the NM, presented in chapter 4. An overall visualisation of the (sub)urban testing session can be looked up in appendix C.

\noindent
An example of the test method is illustrated in figure 6.1.
\begin{figure}[ht]
		\begin{left}
	    	\includegraphics[width=7cm]{graphics/test}
			\caption[Overview test method]{Overview test method.}
			\label{Overview test method}
		\end{left}
\end{figure}
The figures shows the Centroid and CoG of the Drei cell with Cell-Id 1531918 in conjunction with test sample eight representing the real position of the mobile user.

\subsection{Results (sub)urban Area}
The test contains 73 conducted samples randomly distributed in the whole area of interest. 30 cells are incorporated in the test. All the samples were fetched by cruising around with car.

\begin{figure}[ht]
		\begin{center}
	    	\includegraphics[trim=0cm 20.6cm 4.9cm 0cm, width=12.5cm, clip=true]{graphics/cdfSubUrban}
			\caption[CDF (sub)urban testing session]{Overview cumulative distribution function of centroid and CoG localisation in a (sub)urban area.}
			\label{CDF (sub)urban testing session}
		\end{center}
\end{figure}

\noindent
Looking on the CDF graph 6.2, it seems that both methods yields quite similar results, but on the one hand the PE of the centroid approach in a (sub)urban area is about 286 meters, whereas the PE concerning the CoG is about 305 meters. A difference of 20 meters is quite substantial. Moreover, the CoG localisation requires at least three cell borders of each cell. Because of that fact four test samples were unsuccessful, whereas all samples in the centroid case yielded a result.

Considering the error distribution, the R67[m] of the centroid approach is 300 which correlates with a RMS[m] error of about 375. The results of the CoG localisation are 326  (R67[m]) and about 397 (RMS[m] error). Summing up the figures the centroid approach yields better results in this test session than the CoG.

\subsection{Results urban Area}
The test contains 63 conducted samples randomly distributed in the urban area. Eight Cells are incorporated in the test. All the samples were fetched by walking around.

\begin{figure}[ht]
		\begin{center}
	    	\includegraphics[trim=0cm 20.6cm 4.5cm 0cm, width=12.5cm, clip=true]{graphics/cdfUrban}
			\caption[CDF urban testing session]{Overview cumulative distribution function of centroid and CoG localisation in an urban area.}
			\label{CDF urban testing session}
		\end{center}
\end{figure}

Reflecting the CDF graph (urban area) 6.3, the centroid localisation constantly yields better results in comparison with the CoG approach. The R67[m] value of the centroid approach is 187 which results in a RMS[m] error of about 200. In comparison to these figure the results of the CoG are about 241 (R67[m]) and 246 meters (RMS[m] error). Like in the (sub)urban test session, the centroid localisation brings also better results in an urban area.

\subsection{Discussion}
When comparing both testing sessions, the results of the urban testing session are much better. A reason for this is the difference between the cells' diameter in the urban and the (sub)urban area.

\begin{table}[h]
\caption{Overview overall result in a UMTS environment.}
\begin{tabular}{lll}
%
\\
&
\textbf{PE urban}&
\textbf{PE (sub)urban}& \hline
%
Centroid &
169 meters &
291 meters &
%
CoG &
209 meters &
306 meters &
%
Combination &
189 meters &
298 meters & \hline
%
\end{tabular}
\label{tableOverallResults}
\end{table}

The calculation of the diameter considers only cells with ten or more cell borders in order to include only properly recorded cells. The average diameter of cells involved in the urban testing session is about 501 meters, and the \textit{Standard Deviation} (STDEV) is about 170 meters. Comparing with 729 meters average diameter and 519 meters STDEV in the (sub)urban testing session, the cells' diameter differences influence the achieved accuracy, and explains the better accuracy in the urban testing session. 

Relating the cells' diameter difference of about 228 meters, the inaccuracy in the (sub)urban area increases about 114 meters on average because of the higher cell diameter. This trend can be observed in the overall result figures, as listed in table \ref{tableOverallResults}. Anyway analysing the observed differences in the diameter calculation, three aspects have to be followed. First of all the cell diameter can be influenced by a higher cell \textit{density} in urban areas. Another fact is, that the radio propagation is not that much interfered in (sub)urban areas because of a lesser extent of obstacles (e.g. buildings) situated in this regions. Hence BTSs emit longer signal distances, and in succession the cell diameter increases in such areas. Finally the data consistency of the recorded cell structure in (sub)urban areas is not as good as in urban areas, which distorts the results of the calculation.

\begin{figure}[ht]
		\begin{left}
	    	\includegraphics[width=7cm]{graphics/map1521918}
			\caption[Example low cell data consistency]{Example low data consistency\\(Cell-Id:1521918; AC 11902; MNC: 1; MCC: 232; provider: Drei)}
			\label{Example low cell data consistency}
		\end{left}
\end{figure}

As illustrated in figure 6.4}, the cell area is not properly fetched because the samples (green dots) are not even in the cell area. If handovers were fetched again in this area, the cell diameter would be increased because concerning the samples, the cell area is bigger than known in the system.

Summarising the last two paragraphs, the increase of the measured inaccuracy in the (sub)urban area correlates with the lower cell diamter in these areas. Anyway, these figures have to be carefully considered because the reverse engineering process of the cell structure does not exactly reproduce the PLMN's cell structure.

Relating to \citet{Roth2002}, a cell extend up to a few kilometers corresponds to a \textit{microcell} in a UMTS environment. Bringing this in context with this investigation, Drei has deployed a microcell architecture in both testing areas.

Far more difficult to point is the fact that the centroid localisation performs better than the CoG approach. Comparing both methods the centroid yields better results in two-thirds of all cases regarding every single sample, as illustrated in the success ratio diagram \ref{Comparison (sub)urban and urban testing session}. This trend applies to both testing sessions, whereas the measured differences in the urban session is more significant.

\begin{figure}[ht]
 \begin{center}
 \subfigure[][]{
  \label{diagrammSuccessRatio}
  \includegraphics[trim=2.2cm 2.5cm 2.5cm 2.5cm, clip=true, width=6cm]{graphics/diagrammSuccessRatio}}
 \hspace{1cm}
 \subfigure[][]{
  \label{diagrammInaccuracy}
  \includegraphics[trim=2.2cm 2.5cm 2.2cm 2.5cm, clip=true, width=6cm]{graphics/diagrammInaccuracy}}
 \caption[Comparison (sub)urban and urban testing session]{Figure \subref{diagrammSuccessRatio} shows that the centroid method yields better results in two-thirds of all sample cases concerning both testing sessions. Facing the ICR, visualised in \subref{diagrammSuccessRatio}, the accuracy of the centroid approach yields in 54.5\% of cells ((sub)urban) and in 57.1\% of cells (urban) better results than the CoG method. Diagram \subref{diagrammInaccuracy} illustrates the PEs of both methods in each testing sessions as well the combination of centroid and CoG.}
 \label{Comparison (sub)urban and urban testing session}
 \end{center}
\end{figure}

\noindent
The result is a bit different when comparing both localisation methods with the resulted \textit{Inaccuracy per Cell Ratio} (ICR), as noted in figure \ref{tableSuccessRatio}. The CoG localisation brings better results in 45,5\% ((sub)urban session) and 42,9\% (urban session) of cells than the centroid, as visualised in figure \ref{Comparison (sub)urban and urban testing session}.

\begin{table}[h]
\caption{Overview succes ratio.}
\begin{tabular}{lllll}
%
\\
&
\textbf{Centroid}&
\textbf{CoG}&
\textbf{Centroid ICR} &
\textbf{CoG ICR} & \hline
%
(sub)urban &
66,6\% &
33,3\% &
54,5\% &
45,5\% &
%
urban &
67,2\% &
32,8\% &
57,1\% & 
42,9\% & \hline
%
\end{tabular}
\label{tableSuccessRatio}
\end{table}

Because of the relatively low number of test samples per cell there is no distinct tendency apparent. Observing the results concerning the urban session the CoG method yields better results when samples are randomly distributed in the \textbf{whole} cell area. On the other hand the centroid is influenced by the distribution of the cell borders. Moreover, the cell borders are basically fetched on streets. There is in many sample visualisations a correlation between streets, samples and the cell borders recognisable because the tests were also conducted on streets.

However, one advantage of the centroid is that this figure includes more the semantics of the 'environment'. Analysing the centroid formula (chap. 4), the centroid converges on cell boundaries. The recording process in this regards is usually preferred in areas with lots of mobile users. When assuming that only one handover in the north has been fetched, the effect on the centroid would be unsubstantial, if a few handovers in the south were fetched, but the CoG would be influenced by the handover in the north significant. Facing this example, the centroid method approximates its position where much \textbf{activity} (handovers) is going on. Hence this figure may be more suitable than the CoG. However, finding the correct figure or some \textbf{enhanced combinations} requires further \textbf{field tests}.

Field tests in this connection can hardly be simulated. \textbf{In case of a simulation there must be the possibility to check by means of a GIS system whether the position (area) of the sample can be reached by mobile users.} Furthermore having semantical  information about the sample's area gives further decision possibilities whether a sample should be included in the test results or not, and how such samples can be assessed.

\section{Path Loss investigation}
During the FDP concept phase one idea was to involve the signal strength (RSCP) for position determination. The path loss, measured on MTs, has to be investigated because the RSCP value is highly influenced by multipath propagation, but the intended  use of RSCP values in this scope requires stable location-dependent results.

As seen in the system proposal (chap. 4), the signal strength does not find any usage in the position determination approach. The coming sections explain, why the RSCP value cannot be used in this context.

The RSCP localisation idea is sampling a whole area in order to use these samples for the provision of a MPC based on a 'simplified' DCM method in conjunction with the present signal strength (RSCP value), measured on MTs of mobile users. In other words, if a mobile users requests its current position, the LBS client on the MT will send the current cell information in conjunction with the RSCP value to the LT provider. The LT provider then will determine the position depending on  the sample with the highest correlation in regards of the cell information and RSCP value sent by the LBS client programme. This idea must meet two basic requirements:
\begin{itemize}
  \item \textit{Varied and location-dependent distribution of RSCP values}\\
  This requirements brings the possibility to locate mobile users depending on the RSCP value.
  \item \textit{Consistency of RSCP values in time}\\
  The RSCP values should remain the same during the collection of the samples and during the time when positioning is carried out.
\end{itemize}

\noindent
Both requirements were tested in a UMTS and a GSM environment. The UMTS network belongs to Hutchison 3G Austria (Drei), whereas the \textit{Mobilkom Austria AG & Co KG} (A1) operates the used GSM network. Coming figures were conducted with a Nokia N70 in conjunction with the Symbian API (etel.lib and gsmbas.lib) for fetching cell information and signal strength.

Facing firstly the GSM environment, the RSCP value often varies. This can be seen in a logfile listing \ref{logfileGSM}. The last number of the figure represents the \textit{path loss} in \textit{Power Ratio in decibel of the measured power reference to one milliwatt (mW)} (dBm) \citep{dBm2006}. The RSCP value is negative in this regards, but due to visibility quoted positive in the logfile.

\begin{lstlisting}[label=logfileGSM, caption={Overview logfile GSM\\Change Of Signal Strength Occurred (COSSO),\\Change Of Cell Information Occurred (COCIO),\\(Cell-id-AC-MCC-MNC-Long Name-Short Name-Status-Field Strength).}]
2006-07-21 14:37:55.00, COSSO (31109-11902-232-1-A1-3-2-79)
2006-07-21 14:38:03.46, COSSO (31109-11902-232-1-A1-3-2-87)
2006-07-21 14:38:13.82, COSSO (31109-11902-232-1-A1-3-2-86)
2006-07-21 14:38:17.59, COSSO (31109-11902-232-1-A1-3-2-87)
2006-07-21 14:38:25.12, COSSO (31109-11902-232-1-A1-3-2-86)
2006-07-21 14:38:26.92, COSSO (31109-11902-232-1-A1-3-2-84)
2006-07-21 14:38:27.85, COSSO (31109-11902-232-1-A1-3-2-85)
2006-07-21 14:38:32.65, COSSO (31109-11902-232-1-A1-3-2-88)
2006-07-21 14:38:40.18, COSSO (31109-11902-232-1-A1-3-2-91)
2006-07-21 14:38:42.92, COSSO (31109-11902-232-1-A1-3-2-94)
2006-07-21 14:38:48.67, COSSO (31109-11902-232-1-A1-3-2-93)
2006-07-21 14:38:51.39, COSSO (31109-11902-232-1-A1-3-2-94)
2006-07-21 14:38:55.25, COSSO (31109-11902-232-1-A1-3-2-97)
2006-07-21 14:38:56.10, COSSO (31109-11902-232-1-A1-3-2-96)
2006-07-21 14:38:57.04, COSSO (31109-11902-232-1-A1-3-2-94)
\end{lstlisting}

Nevertheless this RSCP value distribution, as quoted in \ref{logfileGSM}, does not suffice because the second requirement, consistency of RSCP values in time, cannot be accomplished. Using the cell (Cell-Id: 31109), listed in \ref{logfileGSM}, and measuring the path loss for eleven days, a distribution, visualised in figure 6.6, is the outcome.

\begin{figure}[ht]
		\begin{left}
	    	\includegraphics[trim=2cm 2.5cm 2cm 2.5cm, width=10cm, clip=true]{graphics/diagrammPathLoss}
			\caption[Path loss distribution in GSM environment]{Path loss distribution in a GSM environment\\
			Cell-Id 31109; AC: 11902; MNC: 1; MCC: 232: provider: A1).}
			\label{Path loss distribution in GSM environment}
		\end{left}
\end{figure}

Investigating diagram 6.6, the path loss varies almost every day. The STDEV of these values is about 11 dBm. So the consistency in time is not given. Subsequently a localisation based on RSCP values is not possible in GSM environments.

Quite different than in GSM environments is the situation regarding UMTS networks. Regarding to \citet[p. 283]{Hansmann2003}, the \textit{International Telecommunications Union for third Generation mobile radio communication} (IMT-2000) specifies with respect to UMTS the data rates besides other characteristics. The least data rate in microcells is defined by 384 kbps \citep{Hansmann2003}. Furthermore the UMTS QoS classes (\textit{Conversional, Streaming, Interactive, Background}) requires a \textbf{constant bandwidth} in order to function correctly.

\begin{lstlisting}[label=logfileUMTS, caption={Overview logfile UMTS\\Change Of Signal Strength Occurred (COSSO),\\Change Of Cell Information Occurred (COCIO),\\(Cell-id-AC-MCC-MNC-Long Name-Short Name-Status-Field Strength).}]
2006-07-15 02:35:36.07, COCIO (1537518-9000-232-10-3 AT-3-2-91)
2006-07-15 02:37:23.51, COCIO (1522418-9000-232-10-3 AT-3-2-91)
2006-07-15 02:38:42.76, COCIO (1542418-9000-232-10-3 AT-3-2-91)
2006-07-15 02:39:35.09, COCIO (1542618-9000-232-10-3 AT-3-2-91)
2006-07-15 02:40:14.34, COCIO (1517618-9000-232-10-3 AT-3-2-91)
2006-07-15 02:40:26.54, COCIO (1522618-9000-232-10-3 AT-3-2-91)
2006-07-15 02:42:54.59, COCIO (1522818-9000-232-10-3 AT-3-2-91)
2006-07-15 02:43:18.48, COCIO (1531718-9000-232-10-3 AT-3-2-91)
\end{lstlisting}

Looking on the logfile \ref{logfileUMTS}, the RSCP value (last figure in brackets), is constant even when handovers (COCIO) occur. The RSCP value is negative in this regards, but due to visibility quoted positive in the logfile.

UMTS provides a constant \textit{bitrate}, and 3G MTs handle therefore the power supply of the antenna to guarentee QoS. Subsequently the RSCP values do not vary significantly, and a location-dependent RSCP value is not given. Generally the RSCP value remains constant even when handovers occur. Regarding the consistency in time the variance is not as high relating to GSM. Due to this applying the RSCP value for positioning is also not possible in UMTS networks.

\noindent
Summarising the aspects, the RSCP value only in conjunction with the current cell information cannot be used to apply a 'simplified' DCM method in either cases (GSM and UMTS).

\section{Discussion}
When reflecting on the facts of the last sections, this concept yields the expected results. Particularly for improving the position determination, and for harmonising the strengths of the centroid and CoG approach, further research is required which is not the scope of this thesis.

The Cell-Id LDT method in UMTS was also investigated in Finland in \citeyear{Kempii2005}. Relating \citet[p. 55]{Kempii2005}, the R67[m] is 499, whereas the R67[m] in this work is 300. 199 meters is a major difference which can only be effected by the cell density in the investigated area because the LDT method is similar. Moreover, \citeauthor{Kempii2005} lists results of the more enhanced DCM method. In comparison with the Cell-Id (R67[m] 499) the DCM method delivers an R67[m] value of 114. 

Seeing the difference between Cell-ID and DCM, there is much potential to improve the localisation in this concept. Nevertheless the Cell-Id does not require a lot of additional functionality, because the current cell information is in any case stored on the SIM card, and \textbf{positioning can be guaranteed}. However, hearing signals from neighbour cells can be a burden, and does directly affect the results of the DCM method \citep[p. 54]{Kempii2005}. More information about the whole testing figures can be looked up in appendix C.

\section{Summary}
This chapter presents the positioning results of the centroid and CoG localisation approach. The centroid method basically yields better results. The average PE in the urban testing session is 169 meters comparing with 291 meters in the (sub)urban testing area. After that the path loss investigation, executed in a GSM and a UMTS environment, demonstrates that the signal strength (RSCP value) in conjunction with the current cell information can not be used in either technologies in order to apply a 'simplified' DCM method. The problem thereby is that the RSCP value is not constant in time and not location-dependent. Ending with a comparison of results achieved in other similar investigations this chapter shows the potential of the FDP concept.